Lu  [1]} present a new task that predicts
a subject together with its associated visual effects, , reflection, dust, and shadow.
They design a framework named Omnimatte that takes a video as the input and approaches this task through self-supervised learning.
However, this method requires other forms of inputs, such as the instance masks, optical flows, and homographies, which are provided by other methods [2]}, [3]}, [4]}.
Fig. REF  shows visual comparisons between SSIS-Track and Omnimatte, where (a) and (b) show that the masks predicted by Omnimatte contain regions of the background, and also do not include the complete shadow regions.
(c) indicates that our SSIS-Track is applicable to static instances while Omnimate fails as also mentioned in [1]}.
Note that Omnimatte takes about three and a half hours to generate the associated masks for this 21-frames video, while our method takes only around 18 seconds in a single RTX 3090 GPU.
